‘Cornelius’ program speeds assessment of readmission risk

A new computer program called Cornelius calculates two risk scores for each newly arrived Vanderbilt University Hospital patient — one estimating the patient’s risk of developing pressure ulcers (bedsores) during the current hospital stay, and the other estimating the patient’s risk of returning to VUH for readmission within 30 days of discharge.

Statistical models for predicting patient outcomes have been appearing in the literature for decades, but almost no one considers using them in clinical practice because the calculations are too laborious.

“I realized we had to close the gap between publishing these models and making them part of the clinical workflow, and that’s what Cornelius is about,” said biostatistician Dan Byrne, M.S., leader of the Cornelius Team and director of Quality Improvement and Program Evaluation with the Department of Biostatistics.

Cornelius’s risk models use a handful of clinical and demographic factors that are typically documented in the electronic medical record within 24 hours of hospital admission.

The models rest on a statistical analysis of some 30,000 VUH patient records. In that analysis, Byrne and Hank Domenico, M.S., biostatistician III, tested more than 400 clinical and demographic variables for correlation with pressure ulcers and readmission.

In a randomized controlled trial of Cornelius’s usefulness, for half of all VUH patients the two risk scores are completely suppressed, hidden from the care team and from researchers. For the other half of patients the scores are taken into account by the care team as they try to discern which patients may benefit from which preventive measures. All arriving VUH patients are randomly assigned to one of the two groups. (Of course, both groups are subject to the same standard of care.) The study began in 2012 and is expected to continue at least another year.

“With the research component of Cornelius, we would like to show the country, if we can, how you can carry translational science all the way into the clinical space in a seamless, efficient way,” said Gordon Bernard, M.D., associate vice chancellor for Clinical and Translational Research, adding that this is very likely the first study anywhere of the effect of system-wide readmission risk stratification.

Bernard’s office is stepping up internal funding for studies of personalized medicine and clinical improvement efforts at Vanderbilt University Medical Center.

“Without randomization and controls hospitals can be fooled into thinking they’ve fixed a problem, when they may instead have merely witnessed a return to the mean that was entirely to be expected.

“Whenever possible, randomization and controls should be used when mounting care improvement efforts,” Bernard said.

As never before, it can pay hospitals to stratify patient risk.

To stimulate cost prevention, Medicare has begun penalizing hospitals for high readmission rates, while passing savings to hospitals when Medicare post-discharge costs are reduced.

They’ve also stopped reimbursing hospitals for treatment of pressure ulcers that develop during admission.

“Cornelius is the first solution of its kind transforming the relationship between data and outcomes,” said Laura Beth Brown, MSN, R.N., vice president of Vanderbilt Health Services.

“What makes it such an innovative project is the ability to predict patient needs in real time. By translating structured and unstructured data into unified intelligence, Cornelius will transform the way we practice health care.”

“Clearer risk stratification should help make health care more precise, more personalized. It’s exciting to think that Cornelius could eventually lead to better prevention for hospital-acquired infections, surgical complications and so on,” Hickson said.

Use of Cornelius risk scores across the hospital has been spearheaded by Pam Jones, DNP, MSN, R.N., VUH chief nursing officer; Beth Anctil, MSN, R.N., director of Transition Management; and Sonya Moore, MSN, R.N., a quality consultant with the Center for Clinical Improvement.